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| Content Provider | ACM Digital Library |
|---|---|
| Author | Dozier, Christopher Guo, Xi Chaudhary, Mark Jackson, Peter Arumainayagam, Yohendran |
| Abstract | This paper describes how an online directory of expert witnesses was created from jury verdict and settlement documents using text mining techniques. We have created an expert witness directory that contains over 100,000 expert profiles, based on approximately 300,000 jury verdict and settlement documents, publicly available professional license information, an expertise taxonomy, and automatic text mining techniques. This directory can be browsed by area of expertise as well as by location and name. In addition, expert profiles are automatically linked to medline articles and jury verdict and settlement documents. The supporting technologies that made this application possible include information extraction from text via regular expression parsing, record linkage through Bayesian based matching, and automatic rule-based classification. To the best of our knowledge, this is the largest expert witness directory of its kind and the first to be built using automatic text mining techniques. |
| Starting Page | 177 |
| Ending Page | 184 |
| Page Count | 8 |
| File Format | |
| ISBN | 1581137478 |
| DOI | 10.1145/1047788.1047830 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2003-06-24 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | Text mining Expert witness Information extraction |
| Content Type | Text |
| Resource Type | Article |
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